Documents disponibles écrits par cet auteur

Application of two Bayesian filters to estimate unknown heat fluxes in a natural convection problem / Marcelo J. Colaço in Journal of heat transfer, Vol 134 N° 9 (Septembre 2012) 

Public ISBD [article]  in Journal of heat transfer > Vol 134 N° 9 (Septembre 2012) . - 10 p. Titre : Application of two Bayesian filters to estimate unknown heat fluxes in a natural convection problem Type de document : texte imprimé Auteurs : Marcelo J. Colaço, Auteur ; Helcio R. B. Orlande, Auteur ; Wellington B. da Silva, Auteur Année de publication : 2012 Article en page(s) : 10 p. Note générale : heat transfer Langues : Anglais (eng) Mots-clés : particle  filter;  Bayesian  inference;  inverse  problems;  natural  convection Index. décimale : 536 Chaleur. Thermodynamique Résumé : Sequential Monte Carlo (SMC) or particle filter methods, which have been originally introduced in the beginning of the 1950s, became very popular in the last few years in the statistical and engineering communities. Such methods have been widely used to deal with sequential Bayesian inference problems in the fields like economics, signal processing, and robotics, among others. SMC methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, named particles. These particles are propagated along time with a simple Sampling Importance distribution. Two advantages of this method are: they do not require the restrictive hypotheses of the Kalman filter, and they can be applied to nonlinear models with non-Gaussian errors. This paper uses two SMC filters, namely the SIR (sampling importance resampling filter) and the ASIR (auxiliary sampling importance resampling filter) to estimate a heat flux on the wall of a square cavity encasing a liquid undergoing natural convection. Measurements, which contain errors, taken at the boundaries of the cavity were used in the estimation process. The mathematical model as well as the initial condition are supposed to have some errors, which were taken into account in the probabilistic evolution model used for the filter. Also, the results using different grid sizes and patterns for the direct and inverse problems were used to avoid the so-called inverse crime. In these results, additional errors were considered due to the different location of the grid points used. The final results were remarkably good when using the ASIR filter. DEWEY : 536 ISSN : 0022-1481 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JHTRAO000134000009 [...] [article] Application of two Bayesian filters to estimate unknown heat fluxes in a natural convection problem [texte imprimé] / Marcelo J. Colaço, Auteur ; Helcio R. B. Orlande, Auteur ; Wellington B. da Silva, Auteur . - 2012 . - 10 p. heat transfer Langues : Anglais (eng) in Journal of heat transfer > Vol 134 N° 9 (Septembre 2012) . - 10 p. Mots-clés : particle  filter;  Bayesian  inference;  inverse  problems;  natural  convection Index. décimale : 536 Chaleur. Thermodynamique Résumé : Sequential Monte Carlo (SMC) or particle filter methods, which have been originally introduced in the beginning of the 1950s, became very popular in the last few years in the statistical and engineering communities. Such methods have been widely used to deal with sequential Bayesian inference problems in the fields like economics, signal processing, and robotics, among others. SMC methods are an approximation of sequences of probability distributions of interest, using a large set of random samples, named particles. These particles are propagated along time with a simple Sampling Importance distribution. Two advantages of this method are: they do not require the restrictive hypotheses of the Kalman filter, and they can be applied to nonlinear models with non-Gaussian errors. This paper uses two SMC filters, namely the SIR (sampling importance resampling filter) and the ASIR (auxiliary sampling importance resampling filter) to estimate a heat flux on the wall of a square cavity encasing a liquid undergoing natural convection. Measurements, which contain errors, taken at the boundaries of the cavity were used in the estimation process. The mathematical model as well as the initial condition are supposed to have some errors, which were taken into account in the probabilistic evolution model used for the filter. Also, the results using different grid sizes and patterns for the direct and inverse problems were used to avoid the so-called inverse crime. In these results, additional errors were considered due to the different location of the grid points used. The final results were remarkably good when using the ASIR filter. DEWEY : 536 ISSN : 0022-1481 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JHTRAO000134000009 [...]

Combining integral transforms and bayesian inference in the simultaneous identification of variable thermal conductivity and thermal capacity in heterogeneous media / Carolina P. Naveira-Cotta in Journal of heat transfer, Vol. 133 N° 11 (Novembre 2011) 

Public ISBD [article]  in Journal of heat transfer > Vol. 133 N° 11 (Novembre 2011) . - pp. [111301/1-10] Titre : Combining integral transforms and bayesian inference in the simultaneous identification of variable thermal conductivity and thermal capacity in heterogeneous media Type de document : texte imprimé Auteurs : Carolina P. Naveira-Cotta, Auteur ; Helcio R. B. Orlande, Auteur ; Renato M. Cotta, Auteur Année de publication : 2012 Article en page(s) : pp. [111301/1-10] Note générale : Physique Langues : Anglais (eng) Mots-clés : Bayes  methods  Composite  materials  Disperse  systems  Eigenvalues  and  eigenfunctions  Gaussian  distribution  Heat  conduction  Inference  mechanisms  Inverse  problems  Inverse  transforms  Markov  processes  Monte  Carlo  methods  Physics  computing  Specific  heat  Thermal  conductivity Index. décimale : 536 Chaleur. Thermodynamique Résumé : This work presents the combined use of the integral transform method, for the direct problem solution, and of Bayesian inference, for the inverse problem analysis, in the simultaneous estimation of spatially variable thermal conductivity and thermal capacity for one-dimensional heat conduction within heterogeneous media. The direct problem solution is analytically obtained via integral transforms and the related eigenvalue problem is solved by the generalized integral transform technique (GITT), offering a fast, precise, and robust solution for the transient temperature field. The inverse problem analysis employs a Markov chain Monte Carlo (MCMC) method, through the implementation of the Metropolis-Hastings sampling algorithm. Instead of seeking the functions estimation in the form of local values for the thermal conductivity and capacity, an alternative approach is employed based on the eigenfunction expansion of the thermophysical properties themselves. Then, the unknown parameters become the corresponding series coefficients for the properties eigenfunction expansions. Simulated temperatures obtained via integral transforms are used in the inverse analysis, for a prescribed concentration distribution of the dispersed phase in a heterogeneous media such as particle filled composites. Available correlations for the thermal conductivity and theory of mixtures relations for the thermal capacity are employed to produce the simulated results with high precision in the direct problem solution, while eigenfunction expansions with reduced number of terms are employed in the inverse analysis itself, in order to avoid the inverse crime. Gaussian distributions were used as priors for the parameter estimation procedure. In addition, simulated results with different randomly generated errors were employed in order to test the inverse analysis robustness. DEWEY : 536 ISSN : 0022-1481 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JHTRAO000133000011 [...] [article] Combining integral transforms and bayesian inference in the simultaneous identification of variable thermal conductivity and thermal capacity in heterogeneous media [texte imprimé] / Carolina P. Naveira-Cotta, Auteur ; Helcio R. B. Orlande, Auteur ; Renato M. Cotta, Auteur . - 2012 . - pp. [111301/1-10]. Physique Langues : Anglais (eng) in Journal of heat transfer > Vol. 133 N° 11 (Novembre 2011) . - pp. [111301/1-10] Mots-clés : Bayes  methods  Composite  materials  Disperse  systems  Eigenvalues  and  eigenfunctions  Gaussian  distribution  Heat  conduction  Inference  mechanisms  Inverse  problems  Inverse  transforms  Markov  processes  Monte  Carlo  methods  Physics  computing  Specific  heat  Thermal  conductivity Index. décimale : 536 Chaleur. Thermodynamique Résumé : This work presents the combined use of the integral transform method, for the direct problem solution, and of Bayesian inference, for the inverse problem analysis, in the simultaneous estimation of spatially variable thermal conductivity and thermal capacity for one-dimensional heat conduction within heterogeneous media. The direct problem solution is analytically obtained via integral transforms and the related eigenvalue problem is solved by the generalized integral transform technique (GITT), offering a fast, precise, and robust solution for the transient temperature field. The inverse problem analysis employs a Markov chain Monte Carlo (MCMC) method, through the implementation of the Metropolis-Hastings sampling algorithm. Instead of seeking the functions estimation in the form of local values for the thermal conductivity and capacity, an alternative approach is employed based on the eigenfunction expansion of the thermophysical properties themselves. Then, the unknown parameters become the corresponding series coefficients for the properties eigenfunction expansions. Simulated temperatures obtained via integral transforms are used in the inverse analysis, for a prescribed concentration distribution of the dispersed phase in a heterogeneous media such as particle filled composites. Available correlations for the thermal conductivity and theory of mixtures relations for the thermal capacity are employed to produce the simulated results with high precision in the direct problem solution, while eigenfunction expansions with reduced number of terms are employed in the inverse analysis itself, in order to avoid the inverse crime. Gaussian distributions were used as priors for the parameter estimation procedure. In addition, simulated results with different randomly generated errors were employed in order to test the inverse analysis robustness. DEWEY : 536 ISSN : 0022-1481 En ligne : http://asmedl.org/getabs/servlet/GetabsServlet?prog=normal&id=JHTRAO000133000011 [...]